HW2_Bradley_Ritchie
Ritchie Bradley
2/16/2022
I installed a package called tidyquant to pull S&P500 data in a much easier manner than I initially started with.tidyquant does have its own built in functions to make very nice plots, but I only used it to get stock prices over a roughly 2-year period. As I am sure you all know, the stock market is heavily analyzed by major companies and determined individuals alike to try and predict what might happen (and ideally make a ton of money in the process). The plots I make will not be nearly as advanced or technical as many out there, but are a way to examine the scope of the stock market and allow for a little more control and personal style on the plots.
First, I used the tidyquant package to retrieve the data from a selected batch of top 10 companies.
#install.packages("tidyquant")
library(tidyquant)## Warning: package 'tidyquant' was built under R version 4.1.2## Loading required package: lubridate##
## Attaching package: 'lubridate'## The following objects are masked from 'package:base':
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## date, intersect, setdiff, union## Loading required package: PerformanceAnalytics## Warning: package 'PerformanceAnalytics' was built under R version 4.1.2## Loading required package: xts## Warning: package 'xts' was built under R version 4.1.2## Loading required package: zoo## Warning: package 'zoo' was built under R version 4.1.2##
## Attaching package: 'zoo'## The following objects are masked from 'package:base':
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## as.Date, as.Date.numeric##
## Attaching package: 'PerformanceAnalytics'## The following object is masked from 'package:graphics':
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## legend## Loading required package: quantmod## Warning: package 'quantmod' was built under R version 4.1.2## Loading required package: TTR## Warning: package 'TTR' was built under R version 4.1.2## Registered S3 method overwritten by 'quantmod':
## method from
## as.zoo.data.frame zoo## == Need to Learn tidyquant? ====================================================
## Business Science offers a 1-hour course - Learning Lab #9: Performance Analysis & Portfolio Optimization with tidyquant!
## </> Learn more at: https://university.business-science.io/p/learning-labs-pro </>library(tidyverse)## -- Attaching packages --------------------------------------- tidyverse 1.3.1 --## v ggplot2 3.3.5 v purrr 0.3.4
## v tibble 3.1.3 v dplyr 1.0.7
## v tidyr 1.1.3 v stringr 1.4.0
## v readr 2.0.0 v forcats 0.5.1## -- Conflicts ------------------------------------------ tidyverse_conflicts() --
## x lubridate::as.difftime() masks base::as.difftime()
## x lubridate::date() masks base::date()
## x dplyr::filter() masks stats::filter()
## x dplyr::first() masks xts::first()
## x lubridate::intersect() masks base::intersect()
## x dplyr::lag() masks stats::lag()
## x dplyr::last() masks xts::last()
## x lubridate::setdiff() masks base::setdiff()
## x lubridate::union() masks base::union()AAPL <- tq_get("AAPL", get = "stock.prices", from = "2020-01-01", to = "2022-02-16") #to retrieve Apple's data from the given time period
MSFT <- tq_get("MSFT", get = "stock.prices", from = "2020-01-01", to = "2022-02-16")#to retrieve Microsoft's data from the given time period
AMZN <- tq_get("AMZN", get = "stock.prices", from = "2020-01-01", to = "2022-02-16")#to retrieve Amazon's data from the given time period
GOOGL <- tq_get("GOOGL", get = "stock.prices", from = "2020-01-01", to = "2022-02-16")#to retrieve Google's data from the given time period
TSLA <- tq_get("TSLA", get = "stock.prices", from = "2020-01-01", to = "2022-02-16")#to retrieve Tesla's data from the given time period (not technically top 5, but fun to include.
library(dplyr)
top5 <- bind_rows(AAPL, MSFT, AMZN, GOOGL, TSLA) #close to the actual top 5 stocks in the S&P500, but not quite. I just chose some of my favorites
#head(top5)I initially tried using a ggplot that would be converted to a plotly graph for interactivity. I had to scrap this plan because I ran into severe issues with my graphs doing it this way. I could get it to make two separate charts, but when I combined them, the range slider did not work properly and it smushed my charts too badly. I tried for a few hours, but working directly with plotly gave much better results.
p_top5 <- ggplot(data = top5, mapping =aes(
x= date, y = volume, color = symbol))+ geom_line()+ ylab("Trade Volume") +
xlab("Date")+ ggtitle("Trade Volume of 5 Major Companies from 01-01-2020 to 01-01-2022")+ scale_x_date(
breaks = scales::breaks_width("5 days"))
p_top5
library("plotly")## Warning: package 'plotly' was built under R version 4.1.2##
## Attaching package: 'plotly'## The following object is masked from 'package:ggplot2':
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## last_plot## The following object is masked from 'package:stats':
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## filter## The following object is masked from 'package:graphics':
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## layoutlibrary("htmlwidgets")## Warning: package 'htmlwidgets' was built under R version 4.1.2#p_top5_plotly <- ggplotly(p_top5)
#p_top5_plotly2 <- p_top5_plotly %>%
# group_by(symbol) %>%
#rangeslider() %>%
#layout(title = NA, xaxis = list(
# rangeselector = list(
# buttons = list(
# count = 1,
# label = "1 mo",
# step = "month",
# stepmode = "backward"), rangeslider = list(type = "date")))) I was trying everything past rangeslider as a last ditch effort.
#p_top5_plotly2Major stocks tend to have significant market volume, while closing price is an interesting snapshot in time for each stock. To create the market volume plot, I used the plot_ly function directly instead of first using ggplot:
t5V_plotly<- plot_ly(top5,
x = ~date,
y = ~volume,
color = ~symbol,
type = 'scatter',
mode = 'lines')
t5V_plotly <- t5V_plotly %>%
rangeslider() %>%
layout(hovermode = "x",
xaxis = list(title=NA),
yaxis = list(title = "Trade Volume"))
ht5V_plotly <- highlight_key(t5V_plotly, ~symbol)#to enable highlighting functionality
#ht5V_plotlyTo create a plot of the stock closing prices I did the same thing:
t5P_plotly<- plot_ly(top5,
x = ~date,
y = ~close,
color = ~symbol,
type = 'scatter',
mode = 'lines')
t5P_plotly <- t5P_plotly %>%
rangeslider() %>%
layout(hovermode = "x",
xaxis = list(title=NA),
yaxis = list(title = "Closing Value"))
#t5P_plotly
ht5P_plotly <- highlight_key(t5P_plotly, ~symbol) #to enable highlighting functionality
#ht5P_plotlyTo combine them, I used the subplot function. I had to do 2 rows to make this plot work.
t5facet_plotly <- subplot(list(ht5V_plotly,ht5P_plotly),
nrows = 2,
shareX = TRUE,
titleX = FALSE) %>%
rangeslider() %>%
layout(hovermode = "x", title = list(text="Trade Volume and Closing Price of 5 Major Companies from 01-01-2020 to 02-16-2022", font = list(size=12), y = .98, x = 0), plot_bgcolor='#E6E6FA', #plot_bgcolor allows us to choose whatever RGB value we want for the background
xaxis = list(title=list(text='Month', font = list(size = 10)),
yaxis = list(title=list(text='Closing Price (bottom), Volume (top)',font = list(size = 10), standoff = 0))))
t5facet_plotlyThere are some obvious issues with this plot, namely the lack of an axis title for the volume graph (top) and closing price graph (bottom). It also lists each stock twice, which adds to the complexity. It would be better if I could combine the Volume and Stock Closing Price for each company on the legend so that you can select the stock you don’t want and remove it from the graph, but I’ve not figured that functionality out yet.